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Converging genetic and epigenetic drivers of paediatric acute lymphoblastic leukaemia identified by an information-theoretic analysis

Abstract

In cancer, linking epigenetic alterations to drivers of transformation has been difficult, in part because DNA methylation analyses must capture epigenetic variability, which is central to tumour heterogeneity and tumour plasticity. Here, by conducting a comprehensive analysis, based on information theory, of differences in methylation stochasticity in samples from patients with paediatric acute lymphoblastic leukaemia (ALL), we show that ALL epigenomes are stochastic and marked by increased methylation entropy at specific regulatory regions and genes. By integrating DNA methylation and single-cell gene-expression data, we arrived at a relationship between methylation entropy and gene-expression variability, and found that epigenetic changes in ALL converge on a shared set of genes that overlap with genetic drivers involved in chromosomal translocations across the disease spectrum. Our findings suggest that an epigenetically driven gene-regulation network, with UHRF1 (ubiquitin-like with PHD and RING finger domains 1) as a central node, links genetic drivers and epigenetic mediators in ALL.

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Fig. 1: Potential energy landscapes explain DNA methylation stochasticity in normal and cancer cells.
Fig. 2: Differential analysis localizes methylation discordance in ALL.
Fig. 3: DNA methylation stochasticity relates to gene expression in ETV6–RUNX1 ALL.
Fig. 4: Methylation discordance and four cytogenetic subtypes of ALL.
Fig. 5: UHRF1 is a target of epigenetic disruption in ALL.
Fig. 6: A plausible regulatory relationship between UHRF1 and in-frame translocation genes identified in ETV6–RUNX1 ALL.

Data availability

DNA-methylation and RNA-seq data are available at the Gene Expression Omnibus repository under the accession number GSE116229.

References

  1. Mullighan, C. G. The molecular genetic makeup of acute lymphoblastic leukemia. Hematology Am. Soc. Hematol. Educ. Program 2012, 389–396 (2012).

    PubMed  Google Scholar 

  2. Hunger, S. P. & Mullighan, C. G. Acute lymphoblastic leukemia in children. N. Engl. J. Med. 373, 1541–1552 (2015).

    CAS  PubMed  Google Scholar 

  3. Grobner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018).

    PubMed  Google Scholar 

  4. Ma, X. et al. Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature 555, 371–376 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Kulis, M. et al. Whole-genome fingerprint of the DNA methylome during human B cell differentiation. Nat. Genet. 47, 746–756 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Figueroa, M.E. et al. Integrated genetic and epigenetic analysis of childhood acute lymphoblastic leukemia. J. Clin. Invest. 123, 3099–3111 (2013).

  7. Nordlund, J. et al. Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia. Genome Biol. 14, r105 (2013).

    PubMed  PubMed Central  Google Scholar 

  8. Mullighan, C. G. et al. CREBBP mutations in relapsed acute lymphoblastic leukaemia. Nature 471, 235–239 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Mar, B. G. et al. Mutations in epigenetic regulators including SETD2 are gained during relapse in paediatric acute lymphoblastic leukaemia. Nat. Commun. 5, 3469 (2014).

    PubMed  Google Scholar 

  10. Milani, L. et al. DNA methylation for subtype classification and prediction of treatment outcome in patients with childhood acute lymphoblastic leukemia. Blood 115, 1214–1225 (2010).

    CAS  PubMed  Google Scholar 

  11. Hogan, L.E. et al. Integrated genomic analysis of relapsed childhood acute lymphoblastic leukemia reveals therapeutic strategies. Blood 118, 5218–5226 (2011).

  12. Lee, S. T. et al. Epigenetic remodeling in B-cell acute lymphoblastic leukemia occurs in two tracks and employs embryonic stem cell-like signatures. Nucleic Acids Res. 43, 2590–2602 (2015).

  13. Wahlberg, P. et al. DNA methylome analysis of acute lymphoblastic leukemia cells reveals stochastic de novo DNA methylation in CpG islands. Epigenomics 8, 1367–1387 (2016).

    CAS  PubMed  Google Scholar 

  14. Jenkinson, G., Pujadas, E., Goutsias, J. & Feinberg, A. P. Potential energy landscapes identify the information-theoretic nature of the epigenome. Nat. Genet. 49, 719–729 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Jenkinson, G., Abante, J., Feinberg, A. P. & Goutsias, J. An information-theoretic approach to the modeling and analysis of whole-genome bisulfite sequencing data. BMC Bioinformatics 19, 87 (2018).

    PubMed  PubMed Central  Google Scholar 

  16. Jenkinson, G., Abante, J., Koldobskiy, M. A., Feinberg, A. P. & Goutsias, J. Ranking genomic features using an information-theoretic measure of epigenetic discordance. BMC Bioinformatics 20, 175 (2019).

    PubMed  PubMed Central  Google Scholar 

  17. Landan, G. et al. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat. Genet. 44, 1207–1214 (2012).

    CAS  PubMed  Google Scholar 

  18. Wang, F. et al. CellMethy: identification of a focal concordantly methylated pattern of CpGs revealed wide differences between normal and cancer tissues. Sci. Rep. 5, 18037 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Lilljebjorn, H. et al. Identification of ETV6-RUNX1-like and DUX4-rearranged subtypes in paediatric B-cell precursor acute lymphoblastic leukaemia. Nat. Commun. 7, 11790 (2016).

    PubMed  PubMed Central  Google Scholar 

  20. Zhang, J. et al. Deregulation of DUX4 and ERG in acute lymphoblastic leukemia. Nat. Genet. 48, 1481–1489 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Tsuzuki, S., Taguchi, O. & Seto, M. Promotion and maintenance of leukemia by ERG. Blood 117, 3858–3868 (2011).

  22. Meissner, A. et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454, 766–770 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Sharov, A. A. et al. Responsiveness of genes to manipulation of transcription factors in ES cells is associated with histone modifications and tissue specificity. BMC Genomics 12, 102 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Heerema, N. A. et al. Dicentric (9;20)(p11;q11) identified by fluorescence in situ hybridization in four pediatric acute lymphoblastic leukemia patients. Cancer Genet. Cytogenet. 92, 111–115 (1996).

    CAS  PubMed  Google Scholar 

  27. Felice, M. S. et al. Prognostic impact of t(1;19)/TCF3–PBX1 in childhood acute lymphoblastic leukemia in the context of Berlin–Frankfurt–Munster-based protocols. Leuk. Lymphoma 52, 1215–1221 (2011).

    PubMed  Google Scholar 

  28. Pui, C. H., Carroll, W. L., Meshinchi, S. & Arceci, R. J. Biology, risk stratification, and therapy of pediatric acute leukemias: an update. J. Clin. Oncol. 29, 551–565 (2011).

    PubMed  Google Scholar 

  29. Bhojwani, D. et al. ETV6-RUNX1-positive childhood acute lymphoblastic leukemia: improved outcome with contemporary therapy. Leukemia 26, 265–270 (2012).

    CAS  PubMed  Google Scholar 

  30. Paulsson, K. et al. The genomic landscape of high hyperdiploid childhood acute lymphoblastic leukemia. Nat. Genet. 47, 672–676 (2015).

    CAS  PubMed  Google Scholar 

  31. Greaves, M. A causal mechanism for childhood acute lymphoblastic leukaemia. Nat. Rev. Cancer 18, 471–484 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. De Braekeleer, E. et al. Acute lymphoblastic leukemia associated with RCSD1ABL1 novel fusion gene has a distinct gene expression profile from BCRABL1 fusion. Leukemia 27, 1422–1424 (2013).

    PubMed  Google Scholar 

  33. Goyama, S. et al. UBASH3B/Sts-1–CBL axis regulates myeloid proliferation in human preleukemia induced by AML1–ETO. Leukemia 30, 728–739 (2016).

    CAS  PubMed  Google Scholar 

  34. Wernicke, C. M. et al. MondoA is highly overexpressed in acute lymphoblastic leukemia cells and modulates their metabolism, differentiation and survival. Leuk. Res. 36, 1185–1192 (2012).

    CAS  PubMed  Google Scholar 

  35. Zhang, R. et al. A possible 5′-NRIP1/UHRF1-3′ fusion gene detected by array CGH analysis in a Ph+ ALL patient. Cancer Genet. 204, 687–691 (2011).

    CAS  PubMed  Google Scholar 

  36. Sidhu, H. & Capalash, N. UHRF1: the key regulator of epigenetics and molecular target for cancer therapeutics. Tumour Biol. https://doi.org/10.1177/1010428317692205 (2017).

  37. Ashraf, W. et al. The epigenetic integrator UHRF1: on the road to become a universal biomarker for cancer. Oncotarget 8, 51946–51962 (2017).

  38. Chow, M. et al. Maintenance and pharmacologic targeting of ROR1 protein levels via UHRF1 in t(1;19) pre-B-ALL. Oncogene 37, 5221–5232 (2018).

  39. Gibcus, J. H. & Dekker, J. The hierarchy of the 3D genome. Mol. Cell 49, 773–782 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Chow, M. L., Kim, D., Kamath, S., Peng, D. & Luu, M. Use of antiviral medications in drug reaction with eosinophilia and systemic symptoms (DRESS): a case of infantile DRESS. Pediatr. Dermatol. 35, e114–e116 (2018).

    PubMed  Google Scholar 

  41. Yan, F. et al. Inhibition effect of siRNA-downregulated UHRF1 on breast cancer growth. Cancer Biother. Radiopharm. 26, 183–189 (2011).

    CAS  PubMed  Google Scholar 

  42. Yan, F., Wang, X., Shao, L., Ge, M. & Hu, X. Analysis of UHRF1 expression in human ovarian cancer tissues and its regulation in cancer cell growth. Tumour Biol. 36, 8887–8893 (2015).

  43. Ge, T. T., Yang, M., Chen, Z., Lou, G. & Gu, T. UHRF1 gene silencing inhibits cell proliferation and promotes cell apoptosis in human cervical squamous cell carcinoma CaSki cells. J. Ovarian Res. 9, 42 (2016).

    PubMed  PubMed Central  Google Scholar 

  44. Iacobucci, I. & Mullighan, C. G. Genetic basis of acute lymphoblastic leukemia. J. Clin. Oncol. 35, 975–983 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Lilljebjorn, H. & Fioretos, T. New oncogenic subtypes in pediatric B-cell precursor acute lymphoblastic leukemia. Blood 130, 1395–1401 (2017).

    PubMed  Google Scholar 

  46. Reddy, K. L. & Feinberg, A. P. Higher order chromatin organization in cancer. Semin. Cancer Biol. 23, 109–115 (2013).

    CAS  PubMed  Google Scholar 

  47. Shen, H. & Laird, P. W. Interplay between the cancer genome and epigenome. Cell 153, 38–55 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Timp, W. et al. Large hypomethylated blocks as a universal defining epigenetic alteration in human solid tumors. Genome Med. 6, 61 (2014).

    PubMed  PubMed Central  Google Scholar 

  49. Duran-Ferrer, M. et al. The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome. Nat. Cancer 1, 1066–1081 (2020).

    PubMed  PubMed Central  Google Scholar 

  50. Pujadas, E. & Feinberg, A. P. Regulated noise in the epigenetic landscape of development and disease. Cell 148, 1123–1131 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Feinberg, A. P., Koldobskiy, M. A. & Gondor, A. Epigenetic modulators, modifiers and mediators in cancer aetiology and progression. Nat. Rev. Genet. 17, 284–299 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Zheng, S. C., Widschwendter, M. & Teschendorff, A. E. Epigenetic drift, epigenetic clocks and cancer risk. Epigenomics 8, 705–719 (2016).

    CAS  PubMed  Google Scholar 

  53. Landau, D. A. et al. Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. Cancer Cell 26, 813–825 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Li, S. et al. Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia. Nat. Med. 22, 792–799 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Pan, H. et al. Epigenomic evolution in diffuse large B-cell lymphomas. Nat. Commun. 6, 6921 (2015).

    CAS  PubMed  Google Scholar 

  56. Chan, T. E., Stumpf, M. P. H. & Babtie, A. C. Gene regulatory network inference from single-cell data using multivariate information measures. Cell Syst. 5, 251–267.e3 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Giovinazzo, H. et al. A high-throughput screen of pharmacologically active compounds for inhibitors of UHRF1 reveals epigenetic activity of anthracycline derivative chemotherapeutic drugs. Oncotarget 10, 3040 (2019).

    PubMed  PubMed Central  Google Scholar 

  58. Lefebvre, J. L., Kostadinov, D., Chen, W. V., Maniatis, T. & Sanes, J. R. Protocadherins mediate dendritic self-avoidance in the mammalian nervous system. Nature 488, 517–521 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. El Hajj, N., Dittrich, M. & Haaf, T. Epigenetic dysregulation of protocadherins in human disease. Semin. Cell Dev. Biol. 69, 172–182 (2017).

    CAS  PubMed  Google Scholar 

  60. Dias, S., Mansson, R., Gurbuxani, S., Sigvardsson, M. & Kee, B. L. E2A proteins promote development of lymphoid-primed multipotent progenitors. Immunity 29, 217–227 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Semerad, C. L., Mercer, E. M., Inlay, M. A., Weissman, I. L. & Murre, C. E2A proteins maintain the hematopoietic stem cell pool and promote the maturation of myelolymphoid and myeloerythroid progenitors. Proc. Natl Acad. Sci. USA 106, 1930–1935 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Hunger, S. P. et al. The t(1;19)(q23;p13) results in consistent fusion of E2A and PBX1 coding sequences in acute lymphoblastic leukemias. Blood 77, 687–693 (1991).

    CAS  PubMed  Google Scholar 

  63. Inaba, T. et al. Fusion of the leucine zipper gene HLF to the E2A gene in human acute B-lineage leukemia. Science 257, 531–534 (1992).

    CAS  PubMed  Google Scholar 

  64. Wu, H., Caffo, B., Jaffee, H. A., Irizarry, R. A. & Feinberg, A. P. Redefining CpG islands using hidden Markov models. Biostatistics 11, 499–514 (2010).

    PubMed  PubMed Central  Google Scholar 

  65. Ernst, J. & Kellis, M. Chromatin-state discovery and genome annotation with ChromHMM. Nat. Protoc. 12, 2478–2492 (2017).

  66. Ernst, J. & Kellis, M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat. Biotechnol. 33, 364–376 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Encode Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Google Scholar 

  68. Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. Numerical Recipes. The Art of Scientific Computing (Cambridge Univ. Press, 2007).

  69. Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 (2001).

    Google Scholar 

  70. Stasinopoulos, D. M. & Rigby, R. A. Generalized additive models for location scale and shape (GAMLSS) in R. J. Stat. Softw. 23, 1–46 (2007).

    Google Scholar 

  71. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  74. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  75. Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Guo, G. et al. Serum-based culture conditions provoke gene expression variability in mouse embryonic stem cells as revealed by single-cell analysis. Cell Rep. 14, 956–965 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Jiao, Y., Widschwendter, M. & Teschendorff, A. E. A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control. Bioinformatics 30, 2360–2366 (2014).

  78. Fisher, R. A Statistical Methods, Experimental Design, and Statistical Inference (Oxford Univ. Press, 1990).

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Acknowledgements

This work was supported by US National Institutes of Health grants R01 CA65438 and DP1 DK119129 to A.P.F., R01 HG006282 to H.J., US National Science Foundation grant CCF-1656201 to J.G., St. Baldrick’s Foundation fellowship and funding from Unravel Pediatric Cancer to M.A.K. M.A.K. is a Damon Runyon–Sohn Pediatric Cancer Fellow supported by the Damon Runyon Cancer Research Foundation (DRSG-15P-16). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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A.P.F. and M.A.K. designed the study and led the biological experiments. A.P.F. and J.G. supervised all aspects of the research. C.L.B., K.R.R. and P.A.B. provided the primary patient material and disease-specific expertise. A.I., C.C. and R.T. performed library preparation and sequencing. V.A.R.D. performed the single-cell RNA-seq experiments. E.P. performed WGBS quality control, preprocessing and bisulfite alignment. H.J. and W.Z. performed statistical analysis of bulk and single-cell RNA-seq data. G.J., J.A. and J.G. developed the data analysis methods. G.J. and J.A. implemented the data analysis methods. A.P.F., G.J., J.G. and M.A.K. analysed the data and wrote the manuscript.

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Correspondence to John Goutsias or Andrew P. Feinberg.

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WGBS samples, statistics and clinical features, ranked lists of genes, bulk RNA-seq results, single-cell RNA-seq data, gene regulatory-network modules, median JSD of comparisons, and more.

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Koldobskiy, M.A., Jenkinson, G., Abante, J. et al. Converging genetic and epigenetic drivers of paediatric acute lymphoblastic leukaemia identified by an information-theoretic analysis. Nat Biomed Eng 5, 360–376 (2021). https://doi.org/10.1038/s41551-021-00703-2

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